In silico prediction of splice-altering single nucleotide variants in the human genome

By Xueqiu Jian, Eric Boerwinkle, Xiaoming Liu. 2014

481 citations recorded by the source corpus.

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.

Subfields: Molecular Biology

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In Molecular Biology

1. Abhishek Niroula
2. Chunlei Wu
3. Chang Li
4. Xiaoming Liu
5. Yibo Dong
6. Yicheng Tu
7. Chang Li
8. Chengcheng Mou

Citations in the corpus

• The Ensembl Variant Effect Predictor (2016, methodology): “The data files necessary for annotation in 80 vertebrate species and many invertebrates are distributed by Ensembl and Ensembl Genomes (Kersey et al. 2014), respectively.”
• dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, methodology): “In addition to the previously supported query of two attached databases, dbscSNV [35] and SPIDEX [36], for predicting splice interrupting SNVs, the companion query program for the downloadable version added support for querying SpliceAI, a third-party database for predicting splice site gain and…”
• dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, methodology): “An attached database called dbscSNV [Jian et al., 2014] that compiled all potential human scSNVs (15,030,459 in total) is distributed along with dbNSFP, and can be searched using the same companion search program of dbNSFP.”
• Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “Recently, a larger dataset containing 1,164 positive and 1,795 negative cases was used to evaluate eight splice-site predictors [Jian et al., 2014].”